Overview

Dataset statistics

Number of variables15
Number of observations5008
Missing cells359
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory450.1 KiB
Average record size in memory92.0 B

Variable types

Numeric9
DateTime1
Categorical5

Alerts

Ruta has a high cardinality: 4124 distinct valuesHigh cardinality
OperadOR has a high cardinality: 2267 distinct valuesHigh cardinality
ac_type has a high cardinality: 2468 distinct valuesHigh cardinality
Registros has a high cardinality: 4700 distinct valuesHigh cardinality
Resumen has a high cardinality: 4857 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with Año_realializadoHigh correlation
Todos_abordo is highly overall correlated with Pasajeros_a_bordo and 3 other fieldsHigh correlation
Pasajeros_a_bordo is highly overall correlated with Todos_abordo and 3 other fieldsHigh correlation
Tripulacion_abordo is highly overall correlated with Todos_abordo and 3 other fieldsHigh correlation
cantidad de fallecidos is highly overall correlated with Todos_abordo and 4 other fieldsHigh correlation
Pasajeros_fallecidos is highly overall correlated with Todos_abordo and 2 other fieldsHigh correlation
Tripulacionfallecida is highly overall correlated with Tripulacion_abordo and 1 other fieldsHigh correlation
Año_realializado is highly overall correlated with Unnamed: 0High correlation
Registros has 272 (5.4%) missing valuesMissing
Resumen has 59 (1.2%) missing valuesMissing
suelo is highly skewed (γ1 = 49.20313053)Skewed
Unnamed: 0 is uniformly distributedUniform
Ruta is uniformly distributedUniform
Registros is uniformly distributedUniform
Resumen is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Pasajeros_a_bordo has 869 (17.4%) zerosZeros
cantidad de fallecidos has 76 (1.5%) zerosZeros
Pasajeros_fallecidos has 1040 (20.8%) zerosZeros
Tripulacionfallecida has 400 (8.0%) zerosZeros
suelo has 4716 (94.2%) zerosZeros

Reproduction

Analysis started2023-05-24 03:51:02.489508
Analysis finished2023-05-24 03:52:33.080778
Duration1 minute and 30.59 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5008
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2503.5
Minimum0
Maximum5007
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-05-24T00:52:34.207227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile250.35
Q11251.75
median2503.5
Q33755.25
95-th percentile4756.65
Maximum5007
Range5007
Interquartile range (IQR)2503.5

Descriptive statistics

Standard deviation1445.8294
Coefficient of variation (CV)0.57752323
Kurtosis-1.2
Mean2503.5
Median Absolute Deviation (MAD)1252
Skewness0
Sum12537528
Variance2090422.7
MonotonicityStrictly increasing
2023-05-24T00:52:34.502249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
3336 1
 
< 0.1%
3343 1
 
< 0.1%
3342 1
 
< 0.1%
3341 1
 
< 0.1%
3340 1
 
< 0.1%
3339 1
 
< 0.1%
3338 1
 
< 0.1%
3337 1
 
< 0.1%
3335 1
 
< 0.1%
Other values (4998) 4998
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
5007 1
< 0.1%
5006 1
< 0.1%
5005 1
< 0.1%
5004 1
< 0.1%
5003 1
< 0.1%
5002 1
< 0.1%
5001 1
< 0.1%
5000 1
< 0.1%
4999 1
< 0.1%
4998 1
< 0.1%

fecha
Date

Distinct4577
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum1908-09-17 00:00:00
Maximum2021-07-06 00:00:00
2023-05-24T00:52:34.827260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:35.137477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ruta
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4124
Distinct (%)82.4%
Missing5
Missing (%)0.1%
Memory size39.2 KiB
Moscow, Russia
 
16
Manila, Philippines
 
15
New York, New York
 
14
Sao Paulo, Brazil
 
13
Cairo, Egypt
 
13
Other values (4119)
4932 

Length

Max length72
Median length49
Mean length20.812712
Min length5

Characters and Unicode

Total characters104126
Distinct characters90
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3687 ?
Unique (%)73.7%

Sample

1st rowFort Myer, Virginia
2nd rowJuvisy-sur-Orge, France
3rd rowAtlantic City, New Jersey
4th rowVictoria, British Columbia, Canada
5th rowOver the North Sea

Common Values

ValueCountFrequency (%)
Moscow, Russia 16
 
0.3%
Manila, Philippines 15
 
0.3%
New York, New York 14
 
0.3%
Sao Paulo, Brazil 13
 
0.3%
Cairo, Egypt 13
 
0.3%
Bogota, Colombia 12
 
0.2%
Rio de Janeiro, Brazil 12
 
0.2%
Near Moscow, Russia 11
 
0.2%
Chicago, Illinois 11
 
0.2%
Tehran, Iran 10
 
0.2%
Other values (4114) 4876
97.4%

Length

2023-05-24T00:52:35.521457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
near 1350
 
9.2%
off 350
 
2.4%
russia 255
 
1.7%
new 229
 
1.6%
brazil 176
 
1.2%
colombia 153
 
1.0%
canada 131
 
0.9%
france 127
 
0.9%
california 117
 
0.8%
mexico 113
 
0.8%
Other values (4153) 11652
79.5%

Most occurring characters

ValueCountFrequency (%)
a 13037
 
12.5%
9703
 
9.3%
e 7073
 
6.8%
i 6567
 
6.3%
n 6545
 
6.3%
r 6035
 
5.8%
o 5367
 
5.2%
, 5210
 
5.0%
l 4000
 
3.8%
s 3530
 
3.4%
Other values (80) 37059
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74113
71.2%
Uppercase Letter 14738
 
14.2%
Space Separator 9704
 
9.3%
Other Punctuation 5357
 
5.1%
Dash Punctuation 105
 
0.1%
Decimal Number 66
 
0.1%
Control 21
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13037
17.6%
e 7073
9.5%
i 6567
8.9%
n 6545
8.8%
r 6035
 
8.1%
o 5367
 
7.2%
l 4000
 
5.4%
s 3530
 
4.8%
t 3112
 
4.2%
u 2756
 
3.7%
Other values (31) 16091
21.7%
Uppercase Letter
ValueCountFrequency (%)
N 2032
13.8%
C 1456
 
9.9%
S 1145
 
7.8%
M 999
 
6.8%
B 952
 
6.5%
A 920
 
6.2%
P 787
 
5.3%
I 720
 
4.9%
R 652
 
4.4%
O 588
 
4.0%
Other values (17) 4487
30.4%
Decimal Number
ValueCountFrequency (%)
0 24
36.4%
1 15
22.7%
2 9
 
13.6%
5 8
 
12.1%
8 3
 
4.5%
7 2
 
3.0%
3 2
 
3.0%
9 2
 
3.0%
6 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 5210
97.3%
. 115
 
2.1%
' 24
 
0.4%
/ 6
 
0.1%
& 1
 
< 0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9703
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
16
76.2%
5
 
23.8%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88851
85.3%
Common 15275
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13037
14.7%
e 7073
 
8.0%
i 6567
 
7.4%
n 6545
 
7.4%
r 6035
 
6.8%
o 5367
 
6.0%
l 4000
 
4.5%
s 3530
 
4.0%
t 3112
 
3.5%
u 2756
 
3.1%
Other values (58) 30829
34.7%
Common
ValueCountFrequency (%)
9703
63.5%
, 5210
34.1%
. 115
 
0.8%
- 105
 
0.7%
0 24
 
0.2%
' 24
 
0.2%
16
 
0.1%
1 15
 
0.1%
) 11
 
0.1%
( 11
 
0.1%
Other values (12) 41
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104084
> 99.9%
None 42
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13037
 
12.5%
9703
 
9.3%
e 7073
 
6.8%
i 6567
 
6.3%
n 6545
 
6.3%
r 6035
 
5.8%
o 5367
 
5.2%
, 5210
 
5.0%
l 4000
 
3.8%
s 3530
 
3.4%
Other values (63) 37017
35.6%
None
ValueCountFrequency (%)
é 14
33.3%
ö 5
 
11.9%
í 4
 
9.5%
ó 4
 
9.5%
á 2
 
4.8%
ï 2
 
4.8%
ô 1
 
2.4%
è 1
 
2.4%
à 1
 
2.4%
ä 1
 
2.4%
Other values (7) 7
16.7%

OperadOR
Categorical

Distinct2267
Distinct (%)45.4%
Missing10
Missing (%)0.2%
Memory size39.2 KiB
Aeroflot
 
253
Military - U.S. Air Force
 
141
Air France
 
74
Deutsche Lufthansa
 
63
United Air Lines
 
44
Other values (2262)
4423 

Length

Max length65
Median length47
Mean length18.957583
Min length3

Characters and Unicode

Total characters94750
Distinct characters87
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1734 ?
Unique (%)34.7%

Sample

1st rowMilitary - U.S. Army
2nd rowMilitary - U.S. Navy
3rd rowPrivate
4th rowMilitary - German Navy
5th rowMilitary - German Navy

Common Values

ValueCountFrequency (%)
Aeroflot 253
 
5.1%
Military - U.S. Air Force 141
 
2.8%
Air France 74
 
1.5%
Deutsche Lufthansa 63
 
1.3%
United Air Lines 44
 
0.9%
China National Aviation Corporation 43
 
0.9%
Military - U.S. Army Air Forces 43
 
0.9%
Pan American World Airways 41
 
0.8%
American Airlines 37
 
0.7%
US Aerial Mail Service 35
 
0.7%
Other values (2257) 4224
84.3%

Length

2023-05-24T00:52:36.001461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
air 1481
 
10.3%
961
 
6.7%
airlines 840
 
5.8%
military 778
 
5.4%
force 557
 
3.9%
airways 453
 
3.1%
u.s 302
 
2.1%
aeroflot 265
 
1.8%
lines 184
 
1.3%
royal 152
 
1.1%
Other values (2079) 8422
58.5%

Most occurring characters

ValueCountFrequency (%)
i 10212
 
10.8%
9421
 
9.9%
r 8849
 
9.3%
a 7786
 
8.2%
e 6780
 
7.2%
n 5528
 
5.8%
A 5083
 
5.4%
o 4380
 
4.6%
l 4079
 
4.3%
s 4000
 
4.2%
Other values (77) 28632
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68181
72.0%
Uppercase Letter 15071
 
15.9%
Space Separator 9422
 
9.9%
Dash Punctuation 939
 
1.0%
Other Punctuation 869
 
0.9%
Open Punctuation 115
 
0.1%
Close Punctuation 115
 
0.1%
Decimal Number 30
 
< 0.1%
Control 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10212
15.0%
r 8849
13.0%
a 7786
11.4%
e 6780
9.9%
n 5528
8.1%
o 4380
6.4%
l 4079
 
6.0%
s 4000
 
5.9%
t 3921
 
5.8%
c 1996
 
2.9%
Other values (28) 10650
15.6%
Uppercase Letter
ValueCountFrequency (%)
A 5083
33.7%
M 1217
 
8.1%
S 1138
 
7.6%
C 910
 
6.0%
F 901
 
6.0%
T 679
 
4.5%
L 661
 
4.4%
U 534
 
3.5%
P 513
 
3.4%
N 496
 
3.3%
Other values (16) 2939
19.5%
Decimal Number
ValueCountFrequency (%)
0 5
16.7%
7 4
13.3%
4 4
13.3%
2 3
10.0%
5 3
10.0%
1 3
10.0%
8 2
 
6.7%
6 2
 
6.7%
9 2
 
6.7%
3 2
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 718
82.6%
/ 109
 
12.5%
' 25
 
2.9%
, 10
 
1.2%
& 6
 
0.7%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
9421
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
6
75.0%
2
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 939
100.0%
Open Punctuation
ValueCountFrequency (%)
( 115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83252
87.9%
Common 11498
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10212
12.3%
r 8849
 
10.6%
a 7786
 
9.4%
e 6780
 
8.1%
n 5528
 
6.6%
A 5083
 
6.1%
o 4380
 
5.3%
l 4079
 
4.9%
s 4000
 
4.8%
t 3921
 
4.7%
Other values (54) 22634
27.2%
Common
ValueCountFrequency (%)
9421
81.9%
- 939
 
8.2%
. 718
 
6.2%
( 115
 
1.0%
) 115
 
1.0%
/ 109
 
0.9%
' 25
 
0.2%
, 10
 
0.1%
6
 
0.1%
& 6
 
0.1%
Other values (13) 34
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94627
99.9%
None 123
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10212
 
10.8%
9421
 
10.0%
r 8849
 
9.4%
a 7786
 
8.2%
e 6780
 
7.2%
n 5528
 
5.8%
A 5083
 
5.4%
o 4380
 
4.6%
l 4079
 
4.3%
s 4000
 
4.2%
Other values (64) 28509
30.1%
None
ValueCountFrequency (%)
é 102
82.9%
á 5
 
4.1%
à 2
 
1.6%
í 2
 
1.6%
ó 2
 
1.6%
ç 2
 
1.6%
ï 2
 
1.6%
ã 1
 
0.8%
ú 1
 
0.8%
ê 1
 
0.8%
Other values (3) 3
 
2.4%

ac_type
Categorical

Distinct2468
Distinct (%)49.4%
Missing13
Missing (%)0.3%
Memory size39.2 KiB
Douglas DC-3
 
333
de Havilland Canada DHC-6 Twin Otter 300
 
81
Douglas C-47A
 
70
Douglas C-47
 
64
Douglas DC-4
 
41
Other values (2463)
4406 

Length

Max length42
Median length36
Mean length18.541542
Min length4

Characters and Unicode

Total characters92615
Distinct characters77
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1863 ?
Unique (%)37.3%

Sample

1st rowWright Flyer III
2nd rowWright Byplane
3rd rowDirigible
4th rowCurtiss seaplane
5th rowZeppelin L-1 (airship)

Common Values

ValueCountFrequency (%)
Douglas DC-3 333
 
6.6%
de Havilland Canada DHC-6 Twin Otter 300 81
 
1.6%
Douglas C-47A 70
 
1.4%
Douglas C-47 64
 
1.3%
Douglas DC-4 41
 
0.8%
Antonov AN-26 35
 
0.7%
Yakovlev YAK-40 35
 
0.7%
Junkers JU-52/3m 30
 
0.6%
De Havilland DH-4 27
 
0.5%
Douglas C-47B 27
 
0.5%
Other values (2458) 4252
84.9%

Length

2023-05-24T00:52:36.509456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
douglas 1130
 
8.3%
boeing 418
 
3.1%
dc-3 387
 
2.8%
lockheed 332
 
2.4%
de 294
 
2.2%
havilland 292
 
2.1%
antonov 288
 
2.1%
canada 159
 
1.2%
otter 146
 
1.1%
ilyushin 142
 
1.0%
Other values (2525) 10025
73.6%

Most occurring characters

ValueCountFrequency (%)
8649
 
9.3%
- 5180
 
5.6%
e 4842
 
5.2%
o 4638
 
5.0%
a 4636
 
5.0%
n 3856
 
4.2%
l 3696
 
4.0%
i 3486
 
3.8%
r 3306
 
3.6%
C 3034
 
3.3%
Other values (67) 47292
51.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46427
50.1%
Uppercase Letter 17900
 
19.3%
Decimal Number 13808
 
14.9%
Space Separator 8650
 
9.3%
Dash Punctuation 5180
 
5.6%
Other Punctuation 264
 
0.3%
Open Punctuation 190
 
0.2%
Close Punctuation 189
 
0.2%
Math Symbol 3
 
< 0.1%
Control 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4842
10.4%
o 4638
10.0%
a 4636
10.0%
n 3856
 
8.3%
l 3696
 
8.0%
i 3486
 
7.5%
r 3306
 
7.1%
s 2917
 
6.3%
t 2357
 
5.1%
u 2217
 
4.8%
Other values (18) 10476
22.6%
Uppercase Letter
ValueCountFrequency (%)
C 3034
16.9%
D 2819
15.7%
A 1901
10.6%
B 1728
9.7%
H 1016
 
5.7%
L 883
 
4.9%
F 796
 
4.4%
S 790
 
4.4%
I 642
 
3.6%
T 620
 
3.5%
Other values (16) 3671
20.5%
Decimal Number
ValueCountFrequency (%)
2 2167
15.7%
0 2103
15.2%
1 2017
14.6%
3 1706
12.4%
4 1704
12.3%
7 1494
10.8%
6 875
6.3%
5 713
 
5.2%
8 664
 
4.8%
9 365
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 185
70.1%
. 76
28.8%
, 2
 
0.8%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
8649
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 5180
100.0%
Open Punctuation
ValueCountFrequency (%)
( 190
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64327
69.5%
Common 28288
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4842
 
7.5%
o 4638
 
7.2%
a 4636
 
7.2%
n 3856
 
6.0%
l 3696
 
5.7%
i 3486
 
5.4%
r 3306
 
5.1%
C 3034
 
4.7%
s 2917
 
4.5%
D 2819
 
4.4%
Other values (44) 27097
42.1%
Common
ValueCountFrequency (%)
8649
30.6%
- 5180
18.3%
2 2167
 
7.7%
0 2103
 
7.4%
1 2017
 
7.1%
3 1706
 
6.0%
4 1704
 
6.0%
7 1494
 
5.3%
6 875
 
3.1%
5 713
 
2.5%
Other values (13) 1680
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92596
> 99.9%
None 17
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8649
 
9.3%
- 5180
 
5.6%
e 4842
 
5.2%
o 4638
 
5.0%
a 4636
 
5.0%
n 3856
 
4.2%
l 3696
 
4.0%
i 3486
 
3.8%
r 3306
 
3.6%
C 3034
 
3.3%
Other values (62) 47273
51.1%
None
ValueCountFrequency (%)
é 12
70.6%
è 4
 
23.5%
  1
 
5.9%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

Registros
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4700
Distinct (%)99.2%
Missing272
Missing (%)5.4%
Memory size39.2 KiB
49
 
3
SU-AFK
 
2
2
 
2
19
 
2
CCCP-45012
 
2
Other values (4695)
4725 

Length

Max length15
Median length6
Mean length6.4940878
Min length1

Characters and Unicode

Total characters30756
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4665 ?
Unique (%)98.5%

Sample

1st rowSC1
2nd rowL-48
3rd row97
4th row61
5th row82

Common Values

ValueCountFrequency (%)
49 3
 
0.1%
SU-AFK 2
 
< 0.1%
2 2
 
< 0.1%
19 2
 
< 0.1%
CCCP-45012 2
 
< 0.1%
101 2
 
< 0.1%
G-ADUZ 2
 
< 0.1%
VH-ABB 2
 
< 0.1%
OK-MCT 2
 
< 0.1%
I-BAUS 2
 
< 0.1%
Other values (4690) 4715
94.1%
(Missing) 272
 
5.4%

Length

2023-05-24T00:52:36.987698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
39
 
0.8%
hk 4
 
0.1%
49 3
 
0.1%
cccp 2
 
< 0.1%
82 2
 
< 0.1%
53 2
 
< 0.1%
cf-tcl 2
 
< 0.1%
12406 2
 
< 0.1%
f-bbdm 2
 
< 0.1%
204 2
 
< 0.1%
Other values (4732) 4772
98.8%

Most occurring characters

ValueCountFrequency (%)
- 3497
 
11.4%
C 2022
 
6.6%
A 1711
 
5.6%
1 1541
 
5.0%
N 1432
 
4.7%
2 1246
 
4.1%
P 1193
 
3.9%
4 1187
 
3.9%
5 1132
 
3.7%
0 1098
 
3.6%
Other values (39) 14697
47.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15946
51.8%
Decimal Number 11081
36.0%
Dash Punctuation 3497
 
11.4%
Other Punctuation 119
 
0.4%
Space Separator 90
 
0.3%
Control 12
 
< 0.1%
Lowercase Letter 10
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2022
 
12.7%
A 1711
 
10.7%
N 1432
 
9.0%
P 1193
 
7.5%
B 718
 
4.5%
F 690
 
4.3%
H 636
 
4.0%
T 611
 
3.8%
E 560
 
3.5%
G 559
 
3.5%
Other values (16) 5814
36.5%
Decimal Number
ValueCountFrequency (%)
1 1541
13.9%
2 1246
11.2%
4 1187
10.7%
5 1132
10.2%
0 1098
9.9%
3 1037
9.4%
6 1026
9.3%
7 1015
9.2%
8 912
8.2%
9 887
8.0%
Lowercase Letter
ValueCountFrequency (%)
l 5
50.0%
y 1
 
10.0%
e 1
 
10.0%
o 1
 
10.0%
w 1
 
10.0%
d 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 114
95.8%
? 5
 
4.2%
Control
ValueCountFrequency (%)
10
83.3%
2
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 3497
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15956
51.9%
Common 14800
48.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2022
 
12.7%
A 1711
 
10.7%
N 1432
 
9.0%
P 1193
 
7.5%
B 718
 
4.5%
F 690
 
4.3%
H 636
 
4.0%
T 611
 
3.8%
E 560
 
3.5%
G 559
 
3.5%
Other values (22) 5824
36.5%
Common
ValueCountFrequency (%)
- 3497
23.6%
1 1541
10.4%
2 1246
 
8.4%
4 1187
 
8.0%
5 1132
 
7.6%
0 1098
 
7.4%
3 1037
 
7.0%
6 1026
 
6.9%
7 1015
 
6.9%
8 912
 
6.2%
Other values (7) 1109
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3497
 
11.4%
C 2022
 
6.6%
A 1711
 
5.6%
1 1541
 
5.0%
N 1432
 
4.7%
2 1246
 
4.1%
P 1193
 
3.9%
4 1187
 
3.9%
5 1132
 
3.7%
0 1098
 
3.6%
Other values (39) 14697
47.8%

Todos_abordo
Real number (ℝ)

Distinct244
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.120807
Minimum0
Maximum644
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:37.478704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median16
Q334.25
95-th percentile117
Maximum644
Range644
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation45.402692
Coefficient of variation (CV)1.4589176
Kurtosis24.044967
Mean31.120807
Median Absolute Deviation (MAD)11
Skewness3.9275976
Sum155853
Variance2061.4044
MonotonicityNot monotonic
2023-05-24T00:52:38.116696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 280
 
5.6%
2 246
 
4.9%
4 202
 
4.0%
5 190
 
3.8%
10 179
 
3.6%
6 174
 
3.5%
7 164
 
3.3%
1 139
 
2.8%
9 130
 
2.6%
11 128
 
2.6%
Other values (234) 3176
63.4%
ValueCountFrequency (%)
0 5
 
0.1%
1 139
2.8%
2 246
4.9%
3 280
5.6%
4 202
4.0%
5 190
3.8%
6 174
3.5%
7 164
3.3%
8 119
2.4%
9 130
2.6%
ValueCountFrequency (%)
644 1
< 0.1%
524 1
< 0.1%
517 1
< 0.1%
394 1
< 0.1%
393 1
< 0.1%
384 1
< 0.1%
356 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
340 1
< 0.1%

Pasajeros_a_bordo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct234
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.882788
Minimum0
Maximum614
Zeros869
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:38.868699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q329
95-th percentile109.65
Maximum614
Range614
Interquartile range (IQR)26

Descriptive statistics

Standard deviation43.052562
Coefficient of variation (CV)1.6014917
Kurtosis25.436588
Mean26.882788
Median Absolute Deviation (MAD)12
Skewness4.0258255
Sum134629
Variance1853.5231
MonotonicityNot monotonic
2023-05-24T00:52:39.367699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 869
 
17.4%
27 262
 
5.2%
4 170
 
3.4%
2 162
 
3.2%
5 140
 
2.8%
3 130
 
2.6%
7 130
 
2.6%
10 128
 
2.6%
9 128
 
2.6%
8 126
 
2.5%
Other values (224) 2763
55.2%
ValueCountFrequency (%)
0 869
17.4%
1 120
 
2.4%
2 162
 
3.2%
3 130
 
2.6%
4 170
 
3.4%
5 140
 
2.8%
6 109
 
2.2%
7 130
 
2.6%
8 126
 
2.5%
9 128
 
2.6%
ValueCountFrequency (%)
614 1
< 0.1%
509 1
< 0.1%
503 1
< 0.1%
381 1
< 0.1%
374 1
< 0.1%
364 1
< 0.1%
338 1
< 0.1%
335 1
< 0.1%
327 1
< 0.1%
316 1
< 0.1%

Tripulacion_abordo
Real number (ℝ)

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4968051
Minimum0
Maximum83
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:39.879699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum83
Range83
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6765019
Coefficient of variation (CV)0.81758089
Kurtosis65.889354
Mean4.4968051
Median Absolute Deviation (MAD)2
Skewness5.085092
Sum22520
Variance13.516666
MonotonicityNot monotonic
2023-05-24T00:52:40.280721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 954
19.0%
4 913
18.2%
2 828
16.5%
1 535
10.7%
5 514
10.3%
6 375
 
7.5%
7 244
 
4.9%
8 173
 
3.5%
9 115
 
2.3%
10 94
 
1.9%
Other values (24) 263
 
5.3%
ValueCountFrequency (%)
0 7
 
0.1%
1 535
10.7%
2 828
16.5%
3 954
19.0%
4 913
18.2%
5 514
10.3%
6 375
 
7.5%
7 244
 
4.9%
8 173
 
3.5%
9 115
 
2.3%
ValueCountFrequency (%)
83 1
 
< 0.1%
61 1
 
< 0.1%
49 1
 
< 0.1%
43 1
 
< 0.1%
41 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
27 1
 
< 0.1%
25 4
0.1%

cantidad de fallecidos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct199
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.29353
Minimum0
Maximum583
Zeros76
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:40.596696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q325
95-th percentile85
Maximum583
Range583
Interquartile range (IQR)21

Descriptive statistics

Standard deviation34.972415
Coefficient of variation (CV)1.5687248
Kurtosis36.920237
Mean22.29353
Median Absolute Deviation (MAD)9
Skewness4.6259543
Sum111646
Variance1223.0698
MonotonicityNot monotonic
2023-05-24T00:52:40.979696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 384
 
7.7%
2 377
 
7.5%
3 363
 
7.2%
4 242
 
4.8%
5 235
 
4.7%
6 176
 
3.5%
7 160
 
3.2%
10 159
 
3.2%
13 132
 
2.6%
9 128
 
2.6%
Other values (189) 2652
53.0%
ValueCountFrequency (%)
0 76
 
1.5%
1 384
7.7%
2 377
7.5%
3 363
7.2%
4 242
4.8%
5 235
4.7%
6 176
3.5%
7 160
3.2%
8 128
 
2.6%
9 128
 
2.6%
ValueCountFrequency (%)
583 1
< 0.1%
520 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
329 1
< 0.1%
301 1
< 0.1%
298 1
< 0.1%
290 1
< 0.1%
275 1
< 0.1%
271 1
< 0.1%

Pasajeros_fallecidos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct190
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.896565
Minimum0
Maximum560
Zeros1040
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:41.435695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q320
95-th percentile79
Maximum560
Range560
Interquartile range (IQR)19

Descriptive statistics

Standard deviation33.256766
Coefficient of variation (CV)1.759937
Kurtosis38.93841
Mean18.896565
Median Absolute Deviation (MAD)8
Skewness4.7628844
Sum94634
Variance1106.0125
MonotonicityNot monotonic
2023-05-24T00:52:41.951700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1040
20.8%
1 308
 
6.2%
18 304
 
6.1%
2 263
 
5.3%
3 193
 
3.9%
4 185
 
3.7%
5 139
 
2.8%
6 133
 
2.7%
8 126
 
2.5%
7 126
 
2.5%
Other values (180) 2191
43.8%
ValueCountFrequency (%)
0 1040
20.8%
1 308
 
6.2%
2 263
 
5.3%
3 193
 
3.9%
4 185
 
3.7%
5 139
 
2.8%
6 133
 
2.7%
7 126
 
2.5%
8 126
 
2.5%
9 118
 
2.4%
ValueCountFrequency (%)
560 1
< 0.1%
505 1
< 0.1%
335 1
< 0.1%
316 1
< 0.1%
307 1
< 0.1%
287 1
< 0.1%
283 1
< 0.1%
278 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%

Tripulacionfallecida
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5597045
Minimum0
Maximum43
Zeros400
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:42.528706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1043418
Coefficient of variation (CV)0.87207852
Kurtosis13.683758
Mean3.5597045
Median Absolute Deviation (MAD)1
Skewness2.5794338
Sum17827
Variance9.636938
MonotonicityNot monotonic
2023-05-24T00:52:42.952697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 1059
21.1%
2 892
17.8%
1 771
15.4%
4 591
11.8%
5 402
 
8.0%
0 400
 
8.0%
6 273
 
5.5%
7 171
 
3.4%
8 130
 
2.6%
9 87
 
1.7%
Other values (18) 232
 
4.6%
ValueCountFrequency (%)
0 400
 
8.0%
1 771
15.4%
2 892
17.8%
3 1059
21.1%
4 591
11.8%
5 402
 
8.0%
6 273
 
5.5%
7 171
 
3.4%
8 130
 
2.6%
9 87
 
1.7%
ValueCountFrequency (%)
43 1
 
< 0.1%
33 1
 
< 0.1%
27 1
 
< 0.1%
25 2
 
< 0.1%
23 6
0.1%
22 5
0.1%
21 2
 
< 0.1%
20 3
0.1%
19 5
0.1%
18 3
0.1%

suelo
Real number (ℝ)

SKEWED  ZEROS 

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7208466
Minimum0
Maximum2750
Zeros4716
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-05-24T00:52:43.477698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2750
Range2750
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.251174
Coefficient of variation (CV)32.106971
Kurtosis2445.2883
Mean1.7208466
Median Absolute Deviation (MAD)0
Skewness49.203131
Sum8618
Variance3052.6922
MonotonicityNot monotonic
2023-05-24T00:52:43.960697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4716
94.2%
2 78
 
1.6%
1 63
 
1.3%
3 21
 
0.4%
4 16
 
0.3%
5 12
 
0.2%
7 10
 
0.2%
8 9
 
0.2%
10 6
 
0.1%
6 6
 
0.1%
Other values (41) 71
 
1.4%
ValueCountFrequency (%)
0 4716
94.2%
1 63
 
1.3%
2 78
 
1.6%
3 21
 
0.4%
4 16
 
0.3%
5 12
 
0.2%
6 6
 
0.1%
7 10
 
0.2%
8 9
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
2750 2
< 0.1%
225 1
< 0.1%
125 2
< 0.1%
113 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
78 1
< 0.1%
71 1
< 0.1%
63 1
< 0.1%
58 1
< 0.1%

Resumen
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4857
Distinct (%)98.1%
Missing59
Missing (%)1.2%
Memory size39.2 KiB
Crashed under unknown circumstances.
 
9
Crashed while en route.
 
8
Crashed while attempting to land.
 
7
Crashed during takeoff.
 
6
Crashed into the sea.
 
5
Other values (4852)
4914 

Length

Max length2669
Median length787
Mean length223.39382
Min length8

Characters and Unicode

Total characters1105576
Distinct characters101
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4813 ?
Unique (%)97.3%

Sample

1st rowDuring a demonstration flight, a U.S. Army flyer flown by Orville Wright nose-dived into the ground from a height of approximately 75 feet, killing Lt. Thomas E. Selfridge, 26, who was a passenger. This was the first recorded airplane fatality in history. One of two propellers separated in flight, tearing loose the wires bracing the rudder and causing the loss of control of the aircraft. Orville Wright suffered broken ribs, pelvis and a leg. Selfridge suffered a crushed skull and died a short time later.
2nd rowEugene Lefebvre was the first pilot to ever be killed in an air accident, after his controls jambed while flying in an air show.
3rd rowFirst U.S. dirigible Akron exploded just offshore at an altitude of 1,000 ft. during a test flight.
4th rowThe first fatal airplane accident in Canada occurred when American barnstormer, John M. Bryant, California aviator was killed.
5th rowThe airship flew into a thunderstorm and encountered a severe downdraft crashing 20 miles north of Helgoland Island into the sea. The ship broke in two and the control car immediately sank drowning its occupants.

Common Values

ValueCountFrequency (%)
Crashed under unknown circumstances. 9
 
0.2%
Crashed while en route. 8
 
0.2%
Crashed while attempting to land. 7
 
0.1%
Crashed during takeoff. 6
 
0.1%
Crashed into the sea. 5
 
0.1%
Crashed shortly after taking off. 5
 
0.1%
Crashed on takeoff. 4
 
0.1%
Shot down by rebel forces. 4
 
0.1%
Crashed under unknown circumstances 4
 
0.1%
Crashed en route. 4
 
0.1%
Other values (4847) 4893
97.7%
(Missing) 59
 
1.2%

Length

2023-05-24T00:52:44.530700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 18463
 
10.1%
of 5544
 
3.0%
a 5456
 
3.0%
and 5444
 
3.0%
to 5429
 
3.0%
in 3682
 
2.0%
crashed 3386
 
1.8%
was 2779
 
1.5%
aircraft 2557
 
1.4%
into 2360
 
1.3%
Other values (11568) 127976
69.9%

Most occurring characters

ValueCountFrequency (%)
179362
16.2%
e 104905
 
9.5%
t 81905
 
7.4%
a 79924
 
7.2%
n 68116
 
6.2%
i 65870
 
6.0%
r 63437
 
5.7%
o 62600
 
5.7%
h 42794
 
3.9%
s 39810
 
3.6%
Other values (91) 316853
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 869373
78.6%
Space Separator 179369
 
16.2%
Uppercase Letter 25294
 
2.3%
Other Punctuation 20624
 
1.9%
Decimal Number 8853
 
0.8%
Dash Punctuation 1645
 
0.1%
Close Punctuation 158
 
< 0.1%
Open Punctuation 140
 
< 0.1%
Final Punctuation 67
 
< 0.1%
Control 33
 
< 0.1%
Other values (4) 20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 104905
12.1%
t 81905
 
9.4%
a 79924
 
9.2%
n 68116
 
7.8%
i 65870
 
7.6%
r 63437
 
7.3%
o 62600
 
7.2%
h 42794
 
4.9%
s 39810
 
4.6%
d 38411
 
4.4%
Other values (30) 221601
25.5%
Uppercase Letter
ValueCountFrequency (%)
T 5796
22.9%
C 2775
11.0%
A 2579
10.2%
S 1531
 
6.1%
F 1286
 
5.1%
M 1207
 
4.8%
I 1063
 
4.2%
P 960
 
3.8%
W 924
 
3.7%
N 861
 
3.4%
Other values (16) 6312
25.0%
Other Punctuation
ValueCountFrequency (%)
. 13487
65.4%
, 5721
27.7%
' 771
 
3.7%
" 362
 
1.8%
/ 170
 
0.8%
: 56
 
0.3%
; 34
 
0.2%
& 17
 
0.1%
% 3
 
< 0.1%
# 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 2668
30.1%
1 1368
15.5%
2 1042
 
11.8%
5 830
 
9.4%
3 820
 
9.3%
4 578
 
6.5%
6 432
 
4.9%
7 416
 
4.7%
8 386
 
4.4%
9 313
 
3.5%
Space Separator
ValueCountFrequency (%)
179362
> 99.9%
  7
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 157
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 139
99.3%
[ 1
 
0.7%
Control
ValueCountFrequency (%)
32
97.0%
1
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 1645
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 67
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 7
100.0%
Other Symbol
ValueCountFrequency (%)
° 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 894667
80.9%
Common 210909
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 104905
11.7%
t 81905
 
9.2%
a 79924
 
8.9%
n 68116
 
7.6%
i 65870
 
7.4%
r 63437
 
7.1%
o 62600
 
7.0%
h 42794
 
4.8%
s 39810
 
4.4%
d 38411
 
4.3%
Other values (56) 246895
27.6%
Common
ValueCountFrequency (%)
179362
85.0%
. 13487
 
6.4%
, 5721
 
2.7%
0 2668
 
1.3%
- 1645
 
0.8%
1 1368
 
0.6%
2 1042
 
0.5%
5 830
 
0.4%
3 820
 
0.4%
' 771
 
0.4%
Other values (25) 3195
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1105434
> 99.9%
None 72
 
< 0.1%
Punctuation 70
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179362
16.2%
e 104905
 
9.5%
t 81905
 
7.4%
a 79924
 
7.2%
n 68116
 
6.2%
i 65870
 
6.0%
r 63437
 
5.7%
o 62600
 
5.7%
h 42794
 
3.9%
s 39810
 
3.6%
Other values (73) 316711
28.7%
Punctuation
ValueCountFrequency (%)
’ 67
95.7%
‘ 3
 
4.3%
None
ValueCountFrequency (%)
é 20
27.8%
á 15
20.8%
í 8
 
11.1%
  7
 
9.7%
ó 3
 
4.2%
° 3
 
4.2%
ö 3
 
4.2%
ã 2
 
2.8%
â 2
 
2.8%
ü 2
 
2.8%
Other values (6) 7
 
9.7%

Año_realializado
Real number (ℝ)

Distinct111
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.8516
Minimum1908
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-05-24T00:52:45.042703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1908
5-th percentile1931
Q11951
median1970
Q31992
95-th percentile2010
Maximum2021
Range113
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.703696
Coefficient of variation (CV)0.012534528
Kurtosis-0.95072008
Mean1970.8516
Median Absolute Deviation (MAD)20
Skewness-0.032020334
Sum9870025
Variance610.27257
MonotonicityIncreasing
2023-05-24T00:52:45.477700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1946 88
 
1.8%
1989 83
 
1.7%
1947 82
 
1.6%
1948 78
 
1.6%
1962 78
 
1.6%
1972 77
 
1.5%
1945 75
 
1.5%
1951 75
 
1.5%
1994 74
 
1.5%
1970 73
 
1.5%
Other values (101) 4225
84.4%
ValueCountFrequency (%)
1908 1
 
< 0.1%
1909 1
 
< 0.1%
1912 1
 
< 0.1%
1913 3
 
0.1%
1915 2
 
< 0.1%
1916 5
 
0.1%
1917 7
 
0.1%
1918 4
 
0.1%
1919 9
0.2%
1920 18
0.4%
ValueCountFrequency (%)
2021 7
 
0.1%
2020 8
 
0.2%
2019 13
0.3%
2018 19
0.4%
2017 15
0.3%
2016 23
0.5%
2015 18
0.4%
2014 23
0.5%
2013 25
0.5%
2012 26
0.5%

Interactions

2023-05-24T00:52:26.569132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:04.663373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:08.121059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:10.693297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:12.851918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:14.814896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:17.273896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:19.602902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:22.824092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:27.219128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:05.680056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:08.419056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:10.915291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:13.066917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:15.060902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:17.564900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:19.859897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:23.257086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:27.680126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:05.984054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:08.744055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:11.148293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:13.286893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:15.288904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:17.837899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:20.226898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:23.629127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:28.283125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:06.303055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:09.045054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:11.381289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:13.510896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:15.519904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:18.157900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:20.595087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:24.064127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:28.810123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:06.598053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:09.342293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:11.602847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:13.709895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:15.751892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:18.373897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:20.945091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:24.408129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:29.311124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:06.921054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:09.650292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:11.836856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:13.930892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:16.045893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:18.588898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:21.304089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:24.803128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:29.586124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:07.240054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:09.956292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:12.059870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:14.160891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:16.342896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:18.827901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:21.609085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:25.175127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:29.860128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:07.512057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:10.231291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:12.269841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:14.383900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:16.621890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:19.070897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:21.908089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:25.534128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:30.144125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:07.808060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:10.462316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:12.484653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:14.593894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:16.903899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:19.350895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:22.454085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T00:52:25.944130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-24T00:52:45.847907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0Todos_abordoPasajeros_a_bordoTripulacion_abordocantidad de fallecidosPasajeros_fallecidosTripulacionfallecidasueloAño_realializado
Unnamed: 01.0000.1700.1530.1070.1100.0980.0400.0271.000
Todos_abordo0.1701.0000.9490.6590.7430.7730.3670.0330.170
Pasajeros_a_bordo0.1530.9491.0000.5120.6970.8190.2380.0160.152
Tripulacion_abordo0.1070.6590.5121.0000.5160.3900.6900.0850.107
cantidad de fallecidos0.1100.7430.6970.5161.0000.9260.669-0.0070.110
Pasajeros_fallecidos0.0980.7730.8190.3900.9261.0000.464-0.0240.098
Tripulacionfallecida0.0400.3670.2380.6900.6690.4641.0000.0360.040
suelo0.0270.0330.0160.085-0.007-0.0240.0361.0000.027
Año_realializado1.0000.1700.1520.1070.1100.0980.0400.0271.000

Missing values

2023-05-24T00:52:30.567118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T00:52:31.327122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-24T00:52:32.248964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0fechaRutaOperadORac_typeRegistrosTodos_abordoPasajeros_a_bordoTripulacion_abordocantidad de fallecidosPasajeros_fallecidosTripulacionfallecidasueloResumenAño_realializado
001908-09-17Fort Myer, VirginiaMilitary - U.S. ArmyWright Flyer IIINaN2111100During a demonstration flight, a U.S. Army flyer flown by Orville Wright nose-dived into the ground from a height of approximately 75 feet, killing Lt. Thomas E. Selfridge, 26, who was a passenger. This was the first recorded airplane fatality in history. One of two propellers separated in flight, tearing loose the wires bracing the rudder and causing the loss of control of the aircraft. Orville Wright suffered broken ribs, pelvis and a leg. Selfridge suffered a crushed skull and died a short time later.1908
111909-09-07Juvisy-sur-Orge, FranceNaNWright ByplaneSC11011000Eugene Lefebvre was the first pilot to ever be killed in an air accident, after his controls jambed while flying in an air show.1909
221912-07-12Atlantic City, New JerseyMilitary - U.S. NavyDirigibleNaN5055050First U.S. dirigible Akron exploded just offshore at an altitude of 1,000 ft. during a test flight.1912
331913-08-06Victoria, British Columbia, CanadaPrivateCurtiss seaplaneNaN1011010The first fatal airplane accident in Canada occurred when American barnstormer, John M. Bryant, California aviator was killed.1913
441913-09-09Over the North SeaMilitary - German NavyZeppelin L-1 (airship)NaN20274141830The airship flew into a thunderstorm and encountered a severe downdraft crashing 20 miles north of Helgoland Island into the sea. The ship broke in two and the control car immediately sank drowning its occupants.1913
551913-10-17Near Johannisthal, GermanyMilitary - German NavyZeppelin L-2 (airship)NaN28274281830Hydrogen gas which was being vented was sucked into the forward engine and ignited causing the airship to explode and burn at 3,000 ft..German Navy's Zeppelin airships L-4 and L-5 were blown out to sea in February 1915, never to be seen again.1913
661915-03-05Tienen, BelgiumMilitary - German NavyZeppelin L-8 (airship)NaN41041170170Crashed into trees while attempting to land after being shot down by British and French aircraft.1915
771915-09-03Off Cuxhaven, GermanyMilitary - German NavyZeppelin L-10 (airship)NaN19274191830Exploded and burned near Neuwerk Island, when hydrogen gas, being vented, was ignited by lightning.1915
881916-07-28Near Jambol, BulgeriaMilitary - German ArmySchutte-Lanz S-L-10 (airship)NaN20274201830Crashed near the Black Sea, cause unknown.1916
991916-09-24Billericay, EnglandMilitary - German NavyZeppelin L-32 (airship)NaN22274221830Shot down by British aircraft crashing in flames.1916
Unnamed: 0fechaRutaOperadORac_typeRegistrosTodos_abordoPasajeros_a_bordoTripulacion_abordocantidad de fallecidosPasajeros_fallecidosTripulacionfallecidasueloResumenAño_realializado
499849982020-08-07Calicut, IndiaAir India ExppressBoeing 737-8HGVT-AXH1901846201820The flight IX344 suffered a runway excursion while landing at Kozhikode-Calicut Airport in heavy rain. The nose section separated from the fuselage after going down a steep slope at the end of the runway. The pilot and copilot were among the dead. Low visibility, wet runway, low cloud base and poor braking action possibly contributed to the accident.2020
499949992020-08-22Juba, South SudanSouth West AviaitonAntonov 26BEX-1268537430The cargo plane lost height shortly after departure from Juba Airport and impacted a farm near Hai Referendum about 3nm southwest of the airport. One passenger survived in critical condition. The plane was chartered by the World Food Program to transport supplies and wages to Wau and Aweil.2020
500050002020-09-25Near Chuguev, UkraineMilitary - Ukraine Air ForceAntonov An26SH76 yellow27207261970The military transport, crashed 1.2 miles from Chuguev air base. The plane was carrying cadets from a nearby air force university on a training flight. The crew may have reported failure of an engine prior to the accident.2020
500150012021-01-09Near Jakarta, IndonesiaSriwijaya AirBoeing 737-524PK-CLC62566625660Sriwijaya Air flight 182 was climbing through 10,900 ft., 11 nm north of Jakarta-Soekarno-Hatta International Airport, over the Java Sea when radar and radio contact was lost. The aircraft then lost height rapidly and impacted the Java Sea. Debris was located near Lancang Island.2021
500250022021-03-02Pieri, SudanSouth Sudan Supreme AirlinesLet L-410UVP-EHK-4274108210820One of the engines on the aircraft failed 10 minutes after takeof. When the plane turned back, the second engine failed.2021
500350032021-03-28Near Butte, AlaskaSoloy HelicoptersEurocopter AS350B3 EcureuilN351SH6515410The sightseeing helicopter crashed after missing the top of a 6,000 ft mountain by just 10 - 15 ft. The crash site was near Knik glacier. The pilot, and four others were killed including Czech billionaire Petr Kellner.2021
500450042021-05-21Near Kaduna, NigeriaMilitary - Nigerian Air ForceBeechcraft B300 King Air 350iNAF203117411740While on final approach, in poor weather conditions, the aircraft crashed and burst into flames less than 10 km from Kaduna Airport. All 11 occupants were killed, incuding General Ibrahim Attahiru, Chief of Staff of the Nigerian Army.2021
500550052021-06-10Near Pyin Oo Lwin, MyanmarMilitary - Myanmar Air ForceBeechcraft 1900D461014122121110The plane was carrying military personnel and monks when it crashed about 300 meters from a steel plant in the Mandalay region. The plane was attempting to land in poor weather conditions and broke into three pieces.2021
500650062021-07-04Patikul, Sulu, PhilippinesMilitary - Philippine Air ForceLockheed C-130H Hercules512596888501833While attempting to land at Jolo Airport, the military transport overran the runway, struck two houses and burst into flames coming to rest on a coconut plantation.2021
500750072021-07-06Palana, RussiaKamchatka Aviation EnterpriseAntonov An 26B-100RA-2608528226282260The passenger plane crashed into the top of a cliff while attempting to land in inclement weather. The debris fell into the sea. Contact was lost with the plane 10 minutes before it was to land.2021